Self-calibrated B1 + and B0 Field Inhomogeneities Estimation (SAFE) in 3D Magnetic Resonance Fingerprinting.

PURPOSE: To develop SAFE, a self-calibrated framework to estimate B1 + and B0 field inhomogeneities directly from conventional magnetic resonance fingerprinting (MRF) acquisitions and improve its quantification accuracy. METHODS: SAFE utilized a two-step approach. First, two physics-informed image markers are extracted from the MRF data to create a magnitude and a phase image that are highly correlated with B1 + and B0 inhomogeneities, respectively. Second, a deep learning (DL) network is applied to map these markers to quantitative field maps. SAFE was tested on 3D Spiral Projection Imaging MRF brain acquisition at 3T, where the network was trained and validated across a multi-site, multi-vendor dataset (N = 358) from healthy volunteers and clinical population. The capability of SAFE to be applied to unseen MRF sequences with different signal preparations and flip-angle trains through adaptively retraining without the need for additional training data was also tested. RESULTS: SAFE achieved normalized-root-mean-square-error within 3% against gold-standard field-calibration scans on both B1 + and B0 maps (N = 32), with high performance remaining on datasets from scanners that the training data were acquired from. T1 and T2 biases were shown to be effectively corrected on healthy volunteers, a patient with brain tumor, and a pediatric subject. Tissue quantification accuracy after SAFE-estimated field correction was validated on a large patient cohort (N = 86). SAFE was also demonstrated to be adaptable to different MRF sequences without acquiring additional training data. CONCLUSION: By combining physics-informed image markers with DL, the proposed SAFE framework enables calibration-free estimation of B1 + and B0 field inhomogeneities at 3T for whole brain MRF.